Farm Level Diversification in Dryland Regions of India

Transcription

1 Farm Level Diversification in Dryland Regions of India Introduction R.P. Singh National Institute of Rural Development Hyderabad It is a known fact that farmers in the dryland regions face various types of risks and income instability due to uncertain climatic conditions and poor resource base. The diversification across crops and fields are the two main strategies followed by the farmers to minimize the risks of income instability. In this paper basically three question have been addressed : 1. What are the determinants of crop diversification? 2. What is the impact of crop and plot diversification on mean income level of households? 3. What is the effect of crop and plot diversification on fluctuations in crop income over time? Plot diversification arises from farmers owning more than one field, leasing in land, sub-ploting within fields, and multiple cropping over cropping season. More land fragmentation implies greater plot diversification. Since, majority of farmers in dryland regions are small and marginal farmers, they have to meet multiple objectives with limited land. Hence, they plan their cropping activities in such a manner that it can ensure them regular income, though low, and minimise risk of crop failure. Land fragmentation has many inherent limitations as there is a feeling that it leads to slow adoption of new technology. But for farmers especially marginal and small, it is risk management strategy for spreading risk to larger areas. Some a studies have been conducted based on country or district level data using gross revenue variability as a measure of crop income instability but they often fail to capture real farm level realities. For example, the study by Barah and Binswanger (1982) using district level data for four states indicates that crop diversification was largely a response to low and unstable rainfall. Moreover crop diversification does significantly reduce gross revenue variability. The effect of diversification has not been adequately addressed with farm level data on crop income stability. Data The present study uses farm level data from three distinct agroclimatic regions covering a period of five years to address the causes and consequences of inter-regional and inter-village differences in diversification behaviour. This data set from the ICRISAT Village Studies pertain to a panel of 30 cultivator households in each of the six villages located in three broad soil, climatic and cropping regions of the semi-arid tropics of India. Methodology Both crop and field diversification are measured by a Simpson index of diversity (Patil and Taillie, 1982). The index is calculated for each farm household i for cropping year t and represents the sum of squared proportional areas allocated to each crop or field : lit = (Wijt) 2 j Where Wijt equals the proportional area of field or crop 'j' to gross cropped area planted by household 'i' in year 't'. The index approaches zero for perfect diversification and is equal to one for a farmer planting only one crop in one field. For easy interpretation the diversification (crop as well as plot

2 diversification) is (I-I it ) so that larger values are associated with increasing diversification. The details of crop and plot diversification in these three regions are given in Table 1. Measuring the level of crop diversification is quite complex as more than 35 percent of the plots are intercropped and to get exact area under each crop is quite difficult. Besides many cultivars of different crops are planted and one should be clear to include them as one crop. Table 1 : Levels of crops and plot diversification in the three SAT regions of India. Measures of diversification Agroclimatic Regions Mahbubnagar Sholapur Akola Crop Diversification (CDI) Index of diversification Coefficient of variation (%) 0.48 (54) Average number of crops Range 3.38 (1-8) Plot Diversification (PDI) Index of diversification Coefficient of variation 0.54 (68) Average number of plots Range 4.60 (1-17) 0.57 (54) 7.40 (1-15) 0.75 (87) (1-27) 0.60 (40) 5.30 (2-13) 0.58 (64) 4.90 (1-21) Correlation between crop and plot diversification Number of observations Note : Pooled across households and cropping years. Ranges and coefficient of variation are reported in parentheses. Source : Reproduced from the "Dimensions of farm Level Diversification in the Semi-Arid Tropics of Rural South India". Determinants of crop Diversification The level of crop diversification, both within and across villages, largely depends on site specific ecological characteristics, the resource endowment of farm households and their personal characteristics, emerging weather conditions, crop rotational requirements, perceived consumption needs of the households and market access. However, all these factors such as crop rotational considerations and consumption targets were not readily available with the data and so they have not been analysed. It was expected that personal characteristics, including risk aversion lead to greater diversification. Risk attitudes for each household were estimated by experimental games described in Binswanger (1978) and Binswanger et al (1982). Changing weather conditions particularly at planting is expected to influence crop choices and levels of diversification. The effect should be stronger in less assured rainfall regions where crops are planted in post-rainy season on residual soil moisture. The resource endowments of the farm household is expected to have a substantial influence on diversification. The access to irrigation, availability of draft power, family labour, land quality, experience and education of the farmers, etc, play an important role in influencing diversification. The details of explanatory variables are given in Table 2.

3 Table 2 : Means and coefficients of variation of the expected determinants of net household crop income in three agro-climatic regions of India's SAT. Determinant Mahbubnagar Sholapur Akola Bullocks (No.) 1.50 (132) Family size (No.) 5.96 (41) Gross cropped area (acres) 3.55 (104) Irrigated area (%) (94) 1.25 (92) 6.55 (43) 7.74 (69) (177) 1.67 (114) 5.82 (46) 6.19 (115) 2.80 (277) Superior soil (%) (65) - Land value (Rs 100/acres) (99) (79) 6.74 (97) Age of the head of family (years) (25) Education of the head of family (years) 1.81 (157) (24) 2.13 (149) (27) 4.00 (98) Risk insurance premium 1.32 (64) 1.36 (66) 0.69 (115) Luck Note : Figures in parentheses are coefficient of variation. Since these three regions have diverse agroclimatic conditions the determinants of farm-level diversification are analysed separately for each region. A regional analysis is supported by F test that rejects at the 5% level, the hypothesis that the estimated regressions coefficients are equal across the three regions. The regression results presented in Table 3 indicate that draft power availability is an important explanator of variation in crop diversification across households within some of the regions. As expected, larger farms with more gross cropped area are more diversified than their smaller counterparts. In contrast, interhousehold differences in family size have little bearing on variation in the levels of diversification. In Sholpur region, resource endowment considerations are less important in explaining diversification which is largely accounted for by differences in land quality, cropping-year conditions, and village disparities within the region.

4 Table 3: Determinants of crop diversification in three agroclimatic regions of India's SAT. Explanatory variable Mahbubnagar Sholapur Akola Bullocks 2.24** (2.31) Family size (-0.24) Gross cropped area 1.17*** (5.22) Irrigated area -0.41*** (-8-24) 2.04 (1.44) (-0.45) 0.26 (2.36) 0.30*** (4.85) 2.20** (2.42) 0.10 (0.31) 0.07 (0.72) 0.49*** (3.95) Superior soils * (1.94) - Land value 0.10*** (2.19) Age (-1.50) Education (-1.34) Risk insurance premium 3.90*** (2.58) Luck (-1.67) Year Dummy ( = 0) (0.10) (1.13) (-0.07) (-0.37) (-1.16) (-0.90) 0.90*** (2.37) 1.54 (1.13) 0.26 (0.49) (-1.12) -1.33*** (3.77) *** (-3.36) *** (-8.91) 0.10 (1.39) 0.06 (0.73) 0.47* (1-73) 1.31 (1.05) (-0.91) (-0.91) (-0.59) -6.82*** (-2.64) -4.56* (-1.74) Village Dummy (Aurepalle, Shirapur, and Kanzara=0) in respective regions (-0.96) 8.48*** (2.98) 1.16 (0.67) Intercept R No. of observations Note: Figure in parentheses are t-values. '***', '**', and '*' denote statistical significance at the 0.01, 0.05, 0.10 level respectively.

5 The influence of irrigated area is also significant and site specific. Limited well irrigation, particularly in the post-rainy season opens up opportunities to grow more crops such as wheat, chickpea, and other pulses. Differences in risk aversion are strongly and positively associated with crop diversification in one region but in other regions do not play a strong role in determining crop diversification. Cropping year and site specific village characteristic influences have a negligible impact on crop diversification in the same region. However, in drought prone area of Sholapur crop diversification appears to be sensitive to changing weather conditions. Diversification and Household Net Crop Income It is almost taken for granted in the risk literature that increasing diversification beyond a certain level leads to reduced risk at the cost of expected net crop income. The size of such a trade-off is an empirical question which is addressed in this section. Household net crop income is defined as returns to land, capital and management, i.e. the cost of all purchased inputs and the value of family labour and owned draft power are deducted from the value of production. Household net crop income averaged Rs. 2,150, 2,190, 2,370 over the five cropping years for Mahbubnagar, Sholapur, and Akola regions respectively. Household net cropping-year income is regressed on crop and plot diversification, land quality (in the Sholapur region), irrigated area, land value, gross cropped area, age and education. Cropping-year dummies and within-region village variables are included to capture the average effects of changes in net crop income over time and between villages in the same region. The diversification indices, GDI and PDI, are also squared and included in the regression to capture possible non-linear effects of diversification on income. Because the effect of the resource base on farm level diversification and ultimately on crop income is sequential, the inclusion of resource base determinants and levels of diversification as independent variables should not lead to simultaneity bias with ordinary least squares estimation. Nonetheless, it is better to draw inferences of association rather than causality from the estimated coefficients in Table 4. The results in Table 4 clearly indicate the importance of land quantity and quality in determining net crop income. In the Mahbubnagar villages, the significant quadratic response between crop diversification and income suggests that substitution away from the dominant crops particularly paddy does result in lost income. In Sholapur and Akola crop diversification does not appear to have reached a level that results in reduced income. With the possible exception of Akola, the results suggest that fragmentation has not yet reached a level where inefficiencies associated with scattered holdings and small plot size noticeably detract from crop income. The significant non-linear effects of plot diversification on income in Mahbubnagar are puzzling but may be explained by some complicated interactions between multiple cropping which is positively associated with plot diversification and irrigation. These effects may not have been fully captured by the irrigated area variable. Diversification and Crop Income Stability The effectiveness of diversification as a self-insurance strategy is evaluated in this section. Stability is measured in a relative context and is identified with a low coefficient of variation for household crop income. Crop income is measured in two ways, gross returns and net returns to family owned resources which include land, capital, family labour and management. All non-marketed outputs and inputs are valued at their respective opportunity costs in measuring gross and net returns. Of the 180 cultivator households in the VLS sample, 134 have data on crop income for each of the five years from to The majority of the other 46 households leased out their land for one or more cropping years; a minority left the sample and were replaced. Therefore, there are 134 cultivators for whom coefficients of variation of household gross and net crop returns are estimated from to CVs are calculated for each household across the five cropping years.

6 Table 4 : Determinants of household net crop income in three agroclimatic regions. Explanatory variable Mahbubnagar Sholapur Akola Crop diversification (CDI) 65.55** (2.86) Plot diversification (PDI) 43.02** (-2.26) CDI squared ** (-2.73) PDI squared 46.07* (1.98) Grossed cropped area *** (10.09) (-0.12) (-0.71) 2.58 (0-19) (1.28) 83.17*** (10.86) (-1.09) (-1.33) 61.53* (1.92) (0.56) *** (15.01) Irrigated area 9.28(1.39) 18.92*** (4.13) Superior soil (1-65) 53.11*** (3.15) - Land value 23.62*** (3.73) Education * (1.69) Age (0.96) Year dummy ( =0) ,12 (0.14) (2.91) ** (2.19) 19-91*** (3.18) (1.59) (-0.33) **** (3.25) ** (2.96) (2.96) (-0.21) (0.49) 27.14** (2.65) (-0.94) (1.26) ** (2.85) Village dummy (Aurepalle Shirapur and Kanzara=0 in their respective regions) (-0.32) (1.62) (-1.91) Intercept R No. of observations Note : Figures in parentheses are 't' values and *** and *** indicate statistical significance at 0.01, 0.05, and.10 level of significance, respectively, t It was also expected that crop income stability is a function of some of the resource base determinants specified in the last two sections. Moreover, differences in crop income stability among farmers can also be attributed to fluctuations in the resource base across cropping years. To assess

7 the impact of diversification on crop income stability, it is important to control for shifts in cultivated area, irrigated area, and land quality over time. This is particularly true for regions such as Sholapur where tenancy is prevalent (Jodha, 1981). Descriptive information on the CVs' of the dependent and independent variables is presented in Table 5. The size of the CVs' at about 40 to 50% is another indication of the riskiness of dryland agriculture in the semi-arid tropics of peninsular India. Fluctuations in gross returns are somewhat less than in net returns. Somewhat surprisingly, both average gross and net returns are less stable in the more heavily irrigated Mahbubnagar region compared to the dryland Sholapur and Akola regions. Table 5 : Description of the variables used in the regression analysis of crop income stability Particular Agroclimatic Region Mahbubnagar Sholapur Akola Mean Range Mean Range Mean Range CV of household gross crop returns (GR) CV of household net crop returns (NR) CV of gross cropped area CV of irrigated area CV of land value CV of superior soils It is believed that crop income stability can be traced not only to the level of the resource base but also to its intertemporal variation. Because crop and plot diversification are themselves functions of the same resource base, the regression results in Table 6 should again be viewed in the context of association rather than causation. It is perhaps heroic to try to explain inter-farm variation in crop income stability within a narrow ecological region based on only five year data. Nonetheless, some of the hypothesized determinants behave as expected and have statistically significant estimated coefficients in Table 6. Crop diversification is negatively and strongly associated with crop income instability in the more dryland Sholapur and Akola regions. A 10 percent proportional increase in the crop diversification index is associated with 4.8 and 13.0 decrease in the CV of net crop returns in Sholapur and Akola respectively. At the margin, crop diversification is about three times more effective in stabilizing net returns in rainfall assured Akola than in drought-prone Sholapur. In Mahbubnagar, plot diversification contributes positively to crop income stability. In contrast, in the Akola and Sholapur villages, the results mildly suggest that plot diversification is associated with greater crop income instability. Production conditions for a crop in these more dryland villages must be quite narrowly defined as diversification across space with the same commodity does not dampen fluctuations in income. This is particularly true in Sholapur where field diversification is positively correlated with gross and net return instability. These results support Walker and Singh who found land sharecropped in Sholapur was of inferior quality and was more likely to be the site of crop failure than owner-operated land. Plot diversification appears to be a resource management strategy rather than an action that offers scope for crop income protection. Other results in Table 6 are also notable. As expected, the CVs' of the resource base determinants are for the most part - the exception being land value in Mahbubnagar - positively associated with crop income instability. Larger farms having more gross cropped area are characterized by greater

8 crop income stability in the three regions. However, the impact of farm size on crop income stability is not large. Evaluated at their arithmetic means, a 10 percent increase in gross cropped area leads on are average to about a 2 percent reduction to the CV of net returns. The evidence with respect to irrigation is not as clear, but it weakly suggests that greater access to irrigation is accompanied by greater instability in net returns particularly in the Akola villages where irrigated land accounts for only 3 percent of the gross cropped area. Table 6 : Determinants of crop income stability. Explanatory Region Mahbubnagar Sholapur Akola GR NR GR NR GR NR Crop diversification 0.10 (0.35) 0.39 (1.32) -0.55** (2.61) 0.52 (2.04) (-1.40) -1571* (-227) Plot diversification (-0.19) -0.65*** (-2.74) 0.18 (0.80) 0.16 (0.58) 0.04 (0.16) 0.34 (0.87) Gross Cropped Area -1.80* (-1.77) -2.11* (-1.94) 0.27 (0.41) (-0.77) (-1.54) -131* (-1.81) CV gross cropped area 0.10 (0.65) 0.42** (2.48) 0.39*** (3.44) 0.21 (1.55) 0.40*** (2.60) 0.18 (0.74) Irrigated area (-0.20) 0.22 (1.04) (-0.55) 0.21 (0.82) 0.90* (1.86) 139* (1.79) CV Irrigated area 0.05 (1.09) 0.05 (0.97) 0.04 (1.40) 0.06 (1.59) 0.10* (1.80) 0.06 (0.66) Superior soils (1.04) CV superior soils (0.63) 0.05 (0.32) 0.21 (1.58) Land value (-0.53) (-1.40) (-0.68) (-0.99) (-0.56) 0.07 (028) CV land value (-0.90) -0.33*** (-3.54) (0.02) (-0.27) (1.18) 0.33 (1.70) Village dummy (-0.48) (-1.21) (-0.95) (-1.20) 4.60 (0.96) 4.21 (055) Intercept R Relying on net returns gives a much sharper picture of the determinants of crop income stability than gross returns in Mahbubnagar. For Sholapur and Akola, either income concept provides more or less the same results.

9 Conclusions Returning to the questions posed at the outset of this paper, it was found that the following conclusions are valid across the three regions of study: 1. Bullock availability significantly determines the level of crop and plot diversification across farm households within a region. This result suggests that bullock hire markets are imperfect across the three regions in meeting seasonally peak period demand requirements for planting and other timely operations. Larger land holdings also stimulate or are associated with plot diversification. Despite their significance, it would take a large change in differences in household resource endowment to have a marked influence on diversification. 2. Crop and plot diversification are not strongly correlated with mean levels of household net crop income. 3. Plot diversification does not offer significant protection against instability in gross crop returns. Besides following inter-regional differences were also observed: 1. The impact of irrigation on crop diversification and income stability hinges upon cropping conditions and the existing level of irrigation. When an expansion in irrigation takes place for rainy season paddy production in a region where many farmers have some access to irrigation, irrigation leads to more specialization in paddy production. In contrast, when an investment in well irrigation occurs in a multicrop predominantly dryland region, it enhances opportunities for further crop diversification particularly in the postrainy season. More profitable production opportunities in turn translate into greater relative variance in net crop returns income and increased crop income instability. The net effect of irrigation on crop diversification and income stability depends on the source of irrigation, ecological location, and level of development of irrigation facilities. 2. Crop and plot diversification fluctuate more in more drought prone regions like Sholapur. Crop diversification appears to be more sensitive to agro-climatic determinants than plot diversification. 3. Different regional ecological conditions offer differential opportunities for risk averse farmers to diversify. Therefore, it is not surprising to find that risk attitudinal differences vary regionally in their effect on diversification. Where they are significant, differences in risk attitudes do not exert a sizeable influence on decisions on diversification; their proportional impact is about the same as differences in draft power availability. 4. Crop diversification effectively imparts stability to net crop returns in the dryland farming Sholapur and Akola villages. In the more highly irrigated Mahbubnagar villages, income instability and crop diversification are not significantly associated. The implications of these conclusions are threefold. First, at present levels of technology it appears that increasing land fragmentation associated with plot diversification does not represent a significant cost in terms of income foregone or a material benefit in the form of enhanced crop income stability. Thus a prospective land consolidation program would not compromise the risk-bearing capacity of farmers, nor would it significantly increase average crop income of farm households at existing levels of technology. These issues require closer scrutiny within a more rigorous household economics framework. Secondly, the emergence of crop diversification as a strong force in protecting net crop returns in the more rainfall-assured Akola region decreases the demand for government risk-reducing policies, such as crop insurance, because farmers can rely on self-insurance through diversification to enhance crop income stability. These results further underscore the emerging consensus that the potential benefits of crop insurance programs will be greater in more risk-prone, dryland regions like Sholapur for three reasons. First, yield variability is the predominant source of risk in these regions (Barah and Binswanger, 1982), yields are more likely to be positively covariate across farmers which augments the efficiency of a homogeneous area approach to dampen fluctuations in crop income (Walker and Jodha, 1982), and third, there is less scope for crop diversification to be an effective self-insurance measure.

10 Lastly, an investment in irrigation may have a negative effect on relative crop income stability when instability is measured with respect to the coefficient of variation. The farm-level effects of irrigation may be markedly different from what obtains at the district, state or national levels. References Barah, B.C and Binswanger, H.P Regional effects of national stabilisation policies : The case study of India. ICRISAT Economics Program Progress Report -37, Patancheru, A. P., India Binswanger, H.P Attitudes. towards risk : Experimental measurement in rural India, Economic Growth Centre, Discussion Paper No. 285, Yale University, New Haven, Connecticut. Jodha, N.S. Agricultural tenancy : fresh evidences from dryland areas in India. Economic and Political Weekly - Review of Agriculture, 16 (52) : A118-A128. Patil, G.P. and Taillie, C Diversity as a concept and its measurement. Journal of American Statistical Association, 77: Walker, T.S. and Jodha, N.S Efficiency of risk management by small farmers and implications for crop insurance. ICRISAT Economics Program Progress Report 45, Patancheru, A. P., India.